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Ledoit-Wolf shrinkage estimator

Source: R/ledoit_wolf_est.R
ledoit_wolf_est.Rd

Compute the Ledoit-Wolf shrinkage estimator for the covariance or correlation matrix.

Usage

ledoit_wolf_est(X, method = "linshrink", res = "cov")

Arguments

X

A data matrix.

method

A character string (default = "linshrink") specifying the method used in shrinkage, includes:

  1. "linshrink": linear shrinkage (Ledoit and Wolf 2004) .

  2. "nlshrink": non-linear shrinkage (Ledoit and Wolf 2015; Ledoit and Wolf 2017) .

See linshrink_cov and nlshrink_cov for details.

res

A character string (default = "cov") specifying the result matrix to be obtained, either the covariance matrix ("cov") or the correlation matrix ("cor").

Value

A numeric matrix.

References

Ledoit O, Wolf M (2004). “A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices.” Journal of Multivariate Analysis, 88(2), 365–411. doi:10.1016/S0047-259X(03)00096-4 .

Ledoit O, Wolf M (2015). “Spectrum Estimation: A Unified Framework for Covariance Matrix Estimation and PCA in Large Dimensions.” Journal of Multivariate Analysis, 139, 360–384. doi:10.1016/j.jmva.2015.04.006 .

Ledoit O, Wolf M (2017). “Numerical Implementation of the QuEST Function.” Computational Statistics & Data Analysis, 115, 199–223. doi:10.1016/j.csda.2017.06.004 .